In the competitive cross-border remittances (aka money transfers) business, artificial intelligence is increasingly becoming a key driver. Yet, there couldn’t be a starker contrast between the manner in which the market leader and the prime challenger have set about using A.I.
On the one hand, the leader has developed an analytics platform that analyzes data in real time on its rivals’ exchange rates, fees, and transfer times from around 1,300 countries, spanning 650-plus currency pairs. The platform has helped identify where the company can alter its fees and rates to be more competitive; slashed the time to access data by 90%; and reduced its expenses by 70%. The leader is using A.I. to become more efficient, tweak its offerings, and alter its pricing, so it can maximize profits.
On the other hand, the online-only challenger has used A.I. to completely rethink the way the business works. It uses the proprietary algorithms it has developed to predict the demand for all the currencies in the 80-plus countries in which it operates. Thanks to A.I., it doesn’t have to move money around, and, instead, maintains bank accounts in each country to cover its transactions. By eliminating the need to transfer money across borders, the upstart has reduced its processing times and offers customers transfers that are 80% to 90% cheaper than rivals. Founded just a decade ago, the challenger accounted for 37% of the U.K. money transfers market in 2021, and boasted a valuation of $12 billion.
Bottom line: The challenger is growing rapidly because of A.I., while the leader is fighting to maintain share despite A.I..
As in the remittances business, so it has been elsewhere. Born-digital challengers—such as Airbnb, Amazon, Google, Spotify, and Uber—create all-new business models and business processes driven by A.I., while most incumbents use the technology to improve their efficiency. As a result, the challengers become market disrupters, wooing customers with new value propositions and challenging the leaders, while the latter only become incrementally better. No wonder CEOs complain that they’re unable to realize the full potential of their A.I. investments. Just 11% of the sample in the 2021 MIT SMR-BCG A.I. study said they had gained “substantial” financial benefits by using A.I.—almost the same as the previous year’s 10%.
The issue is no longer whether companies should adopt A.I., but how they should do so. Our studies show that when organizations explore the use of the technology, they would do well to start from scratch and rethink their business models and business processes, putting A.I. at their core. Doing so will help them gain an advantage over existing rivals as well as protection from disruption.
It isn’t only born-digital companies that can start afresh; incumbents such as Honeywell, John Deere, Rolls-Royce, and Siemens are also learning to do so. CEOs can take three steps to make that happen:
Redesign business models
Companies can try to develop new A.I.-powered business models. For example, the agricultural equipment-maker, John Deere, is designing better products as well as providing smart technology-based services to boost farmers’ profitability, thereby laying the foundations of a new business model. It offers smart machines, which allow its customers to grow more and better crops with fewer pesticides. For instance, John Deere’s vision A.I.-powered LettuceBot uses machine-learning software to distinguish between lettuce plants and weeds. It can do so in under a second, and kill only the latter with a small amount of herbicide, reducing herbicide use on average by 90%.
While John Deere’s cloud-enabled JDLink system allows it to connect and manage all the machines on a farm, it has also built an A.I.-based data platform, John Deere Operations Center, which allows customers to access farm-related data. Farmers can monitor activity in real time, analyze performance, determine how best to utilize equipment, and collaborate with ecosystem partners for insights that help them decide what to plant, where, and when. By providing hardware, software, data, and expertise, the industry leader helps its customers maximize productivity and minimize costs. John Deere currently generates revenues by selling its machines and digital services at a premium, but it could, conceivably, enter into profit-sharing agreements with farmers in the future—a radically different business model.
Instead of using A.I. just to make business processes work more efficiently, companies can use the technology to attain objectives that also create more value. For example, when Starbucks woke up to the fact that the ways in which customers could order drinks—online, in app, in store—had multiplied, it realized that it would have to turn its processes on their head to create a warm customer experience with A.I..
Starbucks had traditionally followed a first-come, first-served drinks-making process, which ran the risk of drinks not being served at the right temperature if customers were going to pick up their drinks without ordering them in stores. Starbucks has therefore decided to use A.I.: Its algorithms will decide the order in which baristas in stores should brew drinks, based on customers’ estimated arrival times and orders. That will help optimize the drink-making process and enhance the customer experience by ensuring that each customer receives the drink at the temperature at which it should be consumed.
Reimagine value chains
To use A.I. effectively, companies have to develop fresh links between organizational functions, internal departments, external partners, and customers. They must conceptualize groups of processes as systems to optimize the use of A.I..
Consider, for instance, the automaker Tesla, which continuously maintains relationships with each of its customers, even updating the software of its vehicles periodically. While its rivals take months to create fresh designs, the challenger improves its products as it studies data. Tesla’s algorithms process data from its fleet of over 2 million cars in real time, and pass on the findings to its cross-functional product development teams. Those data-driven insights enable the teams to develop new versions at unprecedented speed, partly because of the A.I.-powered collaboration that Tesla fosters inside the organization.
Tesla’s A.I.-powered systems allow for the continuous improvement of its manufacturing processes as well. If a customer’s vehicle runs into even a minor problem, such as experiencing vibrations in the car’s windows, the data are communicated in real time to Tesla’s robots on the manufacturing line. They can tweak the installation process immediately while employees carry out tests to check if the noises have been eliminated. In a sense, Tesla has upended the traditional industry value chain, making consumers the starting point of its product development and improvement cycle.
Many business leaders are celebrating their success in bring about incremental improvement in their existing businesses with A.I. while others are embarking on the journey to unlock the full potential of the technology. Reinventing business with A.I. is no longer a hypothetical proposition; in the age of A.I., that may be the only way for every organization to thrive.
Read other columns by François Candelon.
François Candelon is a managing director and senior partner at Boston Consulting Group and the global director of the BCG Henderson Institute.
Bowen Ding is a project leader at BCG and an ambassador at the BCG Henderson Institute.
Su Min Ha is a project leader at BCG and ambassador at the BCG Henderson Institute.
Some companies featured in this column are past or current clients of BCG.
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